Search Results (898)

Sustainable tourism development has emerged as a strategic priority across ASEAN countries, yet the role of green innovation and environmentally responsible investment in shaping tourism outcomes remains underexplored. Existing studies often overlook the spatial interdependencies that characterize regional integration and cross-border environmental dynamics. This study investigates how green patents and green foreign direct investment (FDI) influence sustainable tourism development, both within and across ASEAN nations. Drawing on endogenous growth theory, ecological modernization, and FDI spillover frameworks, the analysis employs a Spatial Durbin Model (SDM) using panel data from 2000 to 2023. The findings reveal that green innovation and green FDI significantly enhance tourism development, with notable spatial spillover effects that benefit neighboring countries. These effects are most pronounced in leading ASEAN economies, where institutional capacity and absorptive readiness amplify the impact of green practices. The relationship is further shaped by economic growth, human capital, and political stability, while environmental degradation and inflation pose constraints. The study underscores the nonlinear and regionally heterogeneous nature of green tourism development, offering policy insights for fostering inclusive, resilient, and environmentally sustainable tourism strategies across ASEAN. Full article

To address the issue of significant perturbations caused by the limited flexibility of clean energy grid integration, along with the combined effects of electric vehicle charging demand and the uncertainty of high-penetration intermittent energy in the integrated energy system (IES), a capacity optimization method for the IES subsystem of a hydrogen-containing chemical park, accounting for strong perturbations, is proposed in the context of the park’s energy usage. Firstly, a typical scenario involving source-load disturbances is characterized using Latin hypercube sampling and Euclidean distance reduction techniques. An energy management strategy for subsystem coordination is then developed. Building on this, a capacity optimization model is established, with the objective of minimizing daily integrated costs, carbon emissions, and system load variance. The Pareto optimal solution set is derived using a non-dominated genetic algorithm, and the optimal allocation case is selected through a combination of ideal solution similarity ranking and a subjective–objective weighting method. The results demonstrate that the proposed approach effectively balances economic efficiency, carbon reduction, and system stability while managing strong perturbations. When compared to relying solely on external hydrogen procurement, the integration of hydrogen storage in chemical production can offset high investment costs and deliver substantial environmental benefits. Full article

To address the challenges of weak power-grid infrastructure, insufficient power supply capacity along mountainous railways, and severe three-phase imbalance caused by imbalanced traction loads at the point of common coupling (PCC), this paper proposes an energy storage configuration method for mountainous railway power grids considering renewable energy integration. First, a distributionally robust chance-constrained energy storage system configuration model is established, with the capacity and rated power of the energy storage system as decision variables, and the investment costs, operational costs, and grid operation costs as the objective function. Subsequently, by linearizing the three-phase AC power flow equations and transforming the model into a directly solvable linear form using conditional value-at-risk (CVaR) theory, the original configuration problem is converted into a mixed-integer linear programming (MILP) formulation. Finally, simulations based on an actual high-altitude mountainous railway power grid validate the economic efficiency and effectiveness of the proposed model. Results demonstrate that energy storage deployment reduces overall system voltage deviation by 40.7% and improves three-phase voltage magnitude imbalance by 16%. Full article

We are living in an era where the demand for Open Access to knowledge is growing and the need for transparency in scientific publishing is becoming imperative. The question that arises at this stage is whether openness in knowledge constitutes the Achilles heel of the once profitable legacy publishing industry or whether it is the Trojan horse of the latter for increasing its revenues. At the same time, the question of whether Open Access publishers can ensure their sustainability through this model remains unanswered. This study implements a multi-year analysis (2019–2023) comparing the performance of Open Access and legacy publishers. Using a set of financial ratios—grouped by profitability, liquidity, efficiency, and solvency, as well as data on firm size (revenues, assets, and employee counts), we assess their financial performance. The results indicate that legacy publishers have enormous scale, stable profitability, and high leverage, but low liquidity and return on equity. On the other hand, OA publishers, although smaller, have higher returns, better liquidity, and almost zero borrowing, but with greater annual volatility. The study discusses that OA publishers, despite their small size, can be as profitable as or even more profitable than traditional publishers, thanks to flexible structures and fast cash flows, but remain vulnerable due to limited resources and the risk of acquisition. Furthermore, legacy publishers maintain their dominance by leveraging their scale, strong brands, and investment capacity while adopting or acquiring OA models, creating a competitive environment where scale and strategic differentiation are decisive. Full article

Unlike widely examined urban settings, heritage-led rural regeneration remains an urgent yet insufficiently explored challenge. Grounded in stimulus–response theory, this study examines how heritage capacity influences the regeneration of traditional villages in Hebei Province, China. Drawing on community-building theory, heritage capacity (stimulus) is conceptualized through five dimensions: Public Participation, Media Platform Construction, Adaptive Reuse, Heritage Industry Development, and Landscape Maintenance. Village regeneration (response) is evaluated across economic, social, and environmental dimensions. Using PLS-SEM analysis of questionnaire data and expert consultations, the study shows that regeneration outcomes arise from an integrated system in which tangible and intangible capacities reinforce each other. It further highlights that the most effective strategy combines priority investment with strategic repositioning. For economic sustainability, Adaptive Reuse and Media Platform Construction serve as immediate drivers, while Heritage Industry Development and Landscape Maintenance provide long-term foundations. For social sustainability, Public Participation and Media Platform Construction act as key enablers by strengthening social connections. For environmental sustainability, Adaptive Reuse offers the most direct benefits, whereas Landscape Maintenance and Public Participation contribute gradual but essential outcomes. This study offers practical guidance for the regeneration of Hebei’s villages, proposing a scalable model for sustainable rural development that has broad implications for similar historical regions worldwide. Full article

The increasing interest in off-grid green hydrogen production has elevated the importance of reliable techno-economic assessment (TEA) tools to support investment and planning decisions. However, limited operational data and inconsistent modeling approaches across existing tools introduce significant uncertainty in cost estimations. This study presents a comprehensive review and comparative analysis of seven TEA tools—ranging from simplified calculators to advanced hourly based simulation platforms—used to estimate the Levelized Cost of Hydrogen (LCOH) in off-grid Hydrogen Production Plants (HPPs). A standardized simulation framework was developed to input consistent technical, economic, and financial parameters across all tools, allowing for a horizontal comparison. Results revealed a substantial spread in LCOH values, from EUR 5.86/kg to EUR 8.71/kg, representing a 49% variation. This discrepancy is attributed to differences in modeling depth, treatment of critical parameters (e.g., electrolyzer efficiency, capacity factor, storage, and inflation), and the tools’ temporal resolution. Tools that included higher input granularity, hourly data, and broader system components tended to produce more conservative (higher) LCOH values, highlighting the cost impact of increased modeling realism. Additionally, the total project cost—more than hydrogen output—was identified as the key driver of LCOH variability across tools. This study provides the first multi-tool horizontal testing protocol, a methodological benchmark for evaluating TEA tools and underscores the need for harmonized input structures and transparent modeling assumptions. These findings support the development of more consistent and reliable economic evaluations for off-grid green hydrogen projects, especially as the sector moves toward commercial scale-up and policy integration. Full article

Karst landscapes, characterized by ecological constraints such as thin soil layers, severe rock desertification, and fragile habitats, require a clear understanding of the mechanisms regulating carbon storage and the impacts of ecological restoration measures. However, current research lacks detailed insights into the specific effects of ecological restoration measures. This study integrates multi-source remote sensing data and adjusts InVEST model parameters to systematically reveal the spatiotemporal evolution of carbon storage and its driving mechanisms in typical karst plateau regions of southwest China under ecological restoration measures. The results indicate: (1) From 2000 to 2020, the carbon stock in the study area increased by 6.09% overall. However, from 2020 to 2025, due to the rapid conversion of forest land into building land and grassland, the carbon stock decreased sharply by 7.69%. (2) Severe rock desertification constrains carbon stock, and afforestation provides significantly higher long-term carbon sink benefits. (3) The spatial heterogeneity of carbon storage is primarily influenced by the combined effects of natural factors (rock desertification, elevation, NDVI) and human factors (POP). Based on the research findings, it is recommended that measures to promote close forests be prioritized in karst regions to protect and restore forest ecosystems. At the same time, local habitat improvement and the establishment of ecological compensation mechanisms should be implemented, and the expansion of building land should be strictly controlled to enhance the stability of ecosystems and their carbon sink functions. These research findings provide a solid scientific basis for enhancing and precisely regulating the carbon sink capacity of fragile karst ecosystems, and are of great significance for formulating scientifically sound and reasonable ecological protection policies. Full article

The development of low-permeability reservoirs offshore entails substantial investment and demands high production capacity for oil and gas. Consequently, the analysis and evaluation of key elements for integrated geological–engineering sweet spots have become essential. This study systematically establishes a coupled analysis methodology for geological and engineering parameters of low-permeability reservoirs, based on Offshore Oilfield A. A comprehensive evaluation framework for geological–engineering sweet spots is proposed, which applies grey relational analysis and the analytic hierarchy process. Twelve geological–engineering sweet spots were analysed, with corresponding parameter weightings determined. Geological sweet spots encompassed factors such as porosity, permeability, and oil saturation, and engineering sweet spots considered Young’s modulus, Poisson’s ratio, fracture factor, and brittleness index. Low-permeability reservoirs were categorised into Classes I, II, III, and IV by establishing indicator factors. Integrating seismic inversion and reservoir numerical simulation methods, we constructed an analysis model. This methodology resolves challenges in evaluating offshore low-permeability reservoirs, enabling rapid and precise sweet spot identification. It provides critical technological support for enhancing oil and gas production efficiency. Full article

Investors demand risk minimization references or at least demonstrator plant operations that are scaled down by a factor of about 25 times less than the manufacturing scale. This causes increased investments of about 30% and a time delay of about 3–5 years. Nevertheless, modern process simulation studies based on experimental model parameter determination at a reduced laboratory scale and process model validation by mini-plant operations with risk assessment studies based on a statistically sound quality by design (QbD) approach should be able to substitute existing methods with less effort in terms of time and cost. This approach is used for a risk assessment study based on an industrial-scale simulated moving bed chromatography separation of m- and p-isomers, including potential enrichment cycles of the simulated moving bed’s (SMB) internal desorbent and the corresponding raffinate and extract distillation columns, and well-documented experimental literature data. The results quantify potential risks within probability ranges for investor decisions quite sufficiently. The benefits of ROI across various annual capacity scales and product magnitudes are evident through reductions of about 30% regarding investment and 3–8 years in terms of time to market, which should motivate the desire to implement these innovative methods more strategically in industrial daily work instead of piloting demonstrator-scale construction and operation. Full article

Global climate change has intensified temperature fluctuations, significantly impacting insect populations. Thermal tolerance has emerged as a critical determinant of species distribution and invasion potential. Liriomyza trifolii, an economically important invasive pest, has been rapidly expanding in southeastern coastal regions of China, gradually displacing its congeners L. sativae and L. huidobrensis. This competitive advantage is closely associated with its superior thermal adaptation strategies. Here, we first examine the temperature-mediated competitive dominance of L. trifolii, then systematically elucidate the physiological, biochemical, and molecular mechanisms underlying its temperature tolerance, revealing its survival strategies under extreme temperatures. Notably, L. trifolii exhibits a lower developmental threshold temperature and higher thermal constant, extending its damage period, while its significantly lower supercooling point confers exceptional overwintering capacity. Physiologically, rapid cold hardening (RCH) enhances cold tolerance through glycerol accumulation and increased fatty acid unsaturation, while heat acclimation improves thermotolerance via a trade-off between developmental processes and reproductive investment. Molecular analyses demonstrate that L. trifolii combines the low-temperature inducible characteristics of L. huidobrensis with the high-temperature responsive advantages of L. sativae in heat shock protein (Hsp) expression patterns. Transcriptomic studies further identify differential expressions of lipid metabolism and chaperone-related genes as key to thermal adaptation. Current research limitations include incomplete understanding of non-Hsp gene regulatory networks and laboratory–field adaptation discrepancies. Future studies should integrate multi-omics approaches with ecological modeling to predict L. trifolii’s expansion under climate change scenarios and develop temperature-based green control strategies. Full article

Renewable Energy Communities (RECs) play a vital role in driving the transition to sustainable energy systems by facilitating inclusive and cost-effective renewable energy production. They empower citizens to actively participate in the energy sector, promote local energy resource sharing, and improve local energy balancing efforts. This study presents a model for investment and operational decision-making within an REC framework, enabling multiple members to invest in renewable energy generation and battery energy storage systems. The model determines optimal capacities for each technology, facilitates energy sharing among members, and evaluates both individual and collective economic benefits through an internal electricity sharing price. By examining various scenarios within an established three-member REC, the research identifies key factors influencing the acceptance of a new member into the community. The findings indicate that the economic advantages of expanding the REC are significantly dependent on the characteristics of the prospective new member. Full article

China’s energy system is transitioning from a state of coal-dependent, low-electrification to a low-carbon, high-electrification paradigm. Carbon emissions have become a central constraint that directly influences generation expansion and transmission investment decisions. This study develops a bottom-up optimization framework integrating dynamic carbon trajectories into a coupled generation–transmission–storage expansion model. Distinct carbon emission trajectories are established on the basis of Guangdong’s allocated carbon budget, and the analysis evaluates the resulting power system structures and transition pathways under each scenario. Results show that Guangdong’s clean energy transition relies on external power imports, nuclear power, and variable renewable energy (VRE), collectively accounting for 87% of generation by 2060. Flexibility requirements expand substantially, with storage capacity rising from 10% of installed VRE in 2030 to 26% in 2060. Critically, under identical cumulative carbon budgets, an accelerated decarbonization pathway achieving earlier peak emissions demonstrates a pivotal economic trade-off: it imposes modestly higher near-term operation costs but delivers significant long-term savings by avoiding prohibitively expensive end-of-period abatement measures. Specifically, advancing the emissions peak from 2030 to 2025 reduces cumulative system costs over the planning horizon by CNY 53.7 billion and lowers the 2060 levelized cost of electricity by 5.2%. Full article

Accelerated climate warming has led to the frequent occurrence of extreme weather events, resulting in high-frequency, large-scale, and highly destructive power outages and electricity shortages, which serve as a wake-up call for the safe and stable operation of the power system. To predict safety risks, this study constructs a baseline scenario and five power security scenarios based on the SWITCH-China model, systematically assessing the impact of external shocks on the power system’s evolution path and carbon reduction economics. The results indicate that external shocks are the key factors influencing the power system’s installed capacity structure and generation mix. The increase in demand forces the substitution of non-fossil energy. In the demand growth scenario, by 2060, wind and solar installed capacity will be 1.034 billion kilowatts higher than in the baseline scenario. Rising fuel costs will accelerate the exit of fossil fuel units. In the fuel cost increase scenario, 765 million kilowatts of coal power were reduced cumulatively across three time points. Wind and solar outages, along with transmission failures, lead to significant local economic investments while also causing inter-provincial carbon transfer. In the wind and solar outage scenario, provinces with a high proportion of wind and solar, such as Guangdong and Guizhou, see an increase in carbon emissions of 31 million tons and 8 million tons, respectively. Conversely, provinces with a lower proportion of wind and solar, such as Inner Mongolia and Xinjiang, reduce carbon emissions by 46 million tons and 39 million tons, respectively. Energy storage development supports the expansion of non-fossil energy in the power system. The study recommends accelerating wind and solar deployment, building a storage system at the scale of hundreds of billions of kilowatt-hours, and optimizing the inter-provincial transmission network to address the dual challenges of power security and carbon neutrality. Full article

This study proposes a technoeconomic model for assessing the profitability of modern greenhouses, with emphasis on hydroponic systems and the integration of combined heat and power (CHP) technology. Given the high share of energy costs in total operating expenses (~35%), the model includes both cultivation and energy subsystems and is implemented in a spreadsheet environment for ease of use. The model calculates Return on Investment (ROI) under various scenarios, considering geographical latitude, CHP capacity, cultivation settings, and energy prices. In the baseline case, the greenhouse ROI is 12%, rising to 14% when CHP is integrated, with CHP itself achieving 24%. Key findings include the identification of optimum CHP sizing (0.5–1.5 MW/ha, depending on latitude) and critical inflection points in ROI behavior associated with latitude and cultivation temperature, driven by the depletion of cooling demand and redistribution of operating modes. The analysis confirms that CHP becomes economically attractive when the Spark Ratio (the electricity price to the natural gas price) exceeds 3, offering enhanced profitability and resilience against energy price volatility. The proposed method is simple, transparent, and suitable for preliminary investment analysis and policy planning in sustainable agri-energy systems. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article